SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing (2403.19615v1)

Abstract: In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian Splatting (SA-GS). While the state-of-the-art method Mip-Splatting needs modifying the training procedure of Gaussian splatting, our method functions at test-time and is training-free. Specifically, SA-GS can be applied to any pretrained Gaussian splatting field as a plugin to significantly improve the field's anti-alising performance. The core technique is to apply 2D scale-adaptive filters to each Gaussian during test time. As pointed out by Mip-Splatting, observing Gaussians at different frequencies leads to mismatches between the Gaussian scales during training and testing. Mip-Splatting resolves this issue using 3D smoothing and 2D Mip filters, which are unfortunately not aware of testing frequency. In this work, we show that a 2D scale-adaptive filter that is informed of testing frequency can effectively match the Gaussian scale, thus making the Gaussian primitive distribution remain consistent across different testing frequencies. When scale inconsistency is eliminated, sampling rates smaller than the scene frequency result in conventional jaggedness, and we propose to integrate the projected 2D Gaussian within each pixel during testing. This integration is actually a limiting case of super-sampling, which significantly improves anti-aliasing performance over vanilla Gaussian Splatting. Through extensive experiments using various settings and both bounded and unbounded scenes, we show SA-GS performs comparably with or better than Mip-Splatting. Note that super-sampling and integration are only effective when our scale-adaptive filtering is activated. Our codes, data and models are available at https://github.com/zsy1987/SA-GS.

• The paper introduces a training-free SA-GS technique that dynamically adapts Gaussian splat scales to significantly reduce aliasing artifacts.
• It employs a 2D scale-adaptive filter combined with super-sampling to maintain scale consistency across diverse rendering settings.
• Experimental results show SA-GS outperforms state-of-the-art 3D Gaussian Splatting methods in visual realism and efficiency without retraining.

Scale-Adaptive Gaussian Splatting for Improved Anti-Aliasing in 3D Rendering

Introduction

The fidelity and efficiency of rendering 3D scenes have been significantly advanced with the use of Gaussian Splatting (GS) methods. These methods, notably 3D Gaussian Splatting (3DGS), offer promising solutions for real-time applications by leveraging explicit Gaussian primitives for scene representation. However, despite the palpable benefits, these techniques grapple with scale inconsistency issues leading to visual artefacts, specifically during rendering phases where the scene's observation parameters vary from those of the training phase. This challenge hampers the potential of GS methods, particularly in applications requiring flexibility across different viewing parameters. Addressing this, the paper introduces a novel approach named Scale-Adaptive Gaussian Splatting (SA-GS), which provides a training-free solution for enhancing anti-aliasing performance in Gaussian Splatting fields without necessitating alterations to the training regimen.

Methodology

SA-GS pivots on the central premise that maintaining scale consistency of Gaussian projections across varying rendering settings can radically mitigate visual artefacts. To achieve this, the method dynamically adjusts the scale of Gaussian splats based on the rendering parameters, specifically factoring in changes in observation resolution and camera distance. The core component of the method – the 2D scale-adaptive filter – directly modifies the representation of Gaussian primitives during the rendering test time, accordingly maintaining scale fidelity to the training phase without the need for additional training or manual intervention.

In tandem, to further ameliorate the challenge of aliasing, particularly salient when zooming out or observing scenes at lower rendering frequencies, SA-GS integrates conventional anti-aliasing techniques, notably super-sampling and its analytical extension, integration. By aligning the sampling frequency with the scene's content frequency, these techniques reduce aliasing effects, albeit their effectiveness is fundamentally contingent on the preliminary resolution of scale inconsistencies via the 2D scale-adaptive filter.

Experimental Evaluation

Extensive experiments underscore the efficacy of SA-GS across various settings and datasets, including both bounded and unbounded scenarios. The method was rigorously compared against state-of-the-art counterparts, demonstrating its ability to resolve artefacts inherent in vanilla 3DGS and outperform existing solutions in terms of visual fidelity. Remarkably, it achieved these without the necessity of re-training or amending the existing training pipeline, presenting a compelling advantage for real-time rendering applications.

Implications and Future Work

The introduction of SA-GS marks a significant stride in tackling scale inconsistency and aliasing issues in Gaussian Splatting fields, consequently refining visual quality and rendering efficiency. Its training-free nature and compatibility with existing 3DGS models further accentuate its applicability and potential for widespread adoption.

Looking ahead, the exploration into advanced filtering techniques that could operate synergistically with the 2D scale-adaptive filter presents an intriguing avenue for research. Additionally, examining the implications of SA-GS in dynamic scenes and its integration into more complex rendering pipelines could further extend its utility and impact.

Conclusion

Scale-Adaptive Gaussian Splatting (SA-GS) offers a novel, effective approach to enhancing anti-aliasing in Gaussian Splatting fields, addressing critical challenges of scale inconsistency and aliasing without necessitating alterations to the training process. Its potential implications for advancing 3D rendering technologies, particularly in real-time applications, are substantial, marking an important contribution to the field.